--- obsidian-jupyter: interpreter: interpreter-path ---

Default Notebook Extensions for Better Presentations:

Present working directory path

pwd

'C:\\Users\\Administrator\\Documents\\GitHub\\DSAIML\\00-Python\\00-notebooks'

from sklearn import datasets
import pandas as pd
wine_data = pd.DataFrame(datasets.load_wine().data)
wine_data.columns = datasets.load_wine().feature_names
wine_data.head(5)

	alcohol	malic_acid	ash	alcalinity_of_ash	magnesium	total_phenols	flavanoids	nonflavanoid_phenols	proanthocyanins	color_intensity	hue	od280/od315_of_diluted_wines	proline
0	14.23	1.71	2.43	15.6	127.0	2.80	3.06	0.28	2.29	5.64	1.04	3.92	1065.0
1	13.20	1.78	2.14	11.2	100.0	2.65	2.76	0.26	1.28	4.38	1.05	3.40	1050.0
2	13.16	2.36	2.67	18.6	101.0	2.80	3.24	0.30	2.81	5.68	1.03	3.17	1185.0
3	14.37	1.95	2.50	16.8	113.0	3.85	3.49	0.24	2.18	7.80	0.86	3.45	1480.0
4	13.24	2.59	2.87	21.0	118.0	2.80	2.69	0.39	1.82	4.32	1.04	2.93	735.0

Markdown syntax

markdown syntax: https://www.markdownguide.org/basic-syntax/

Accessing/loading a files

If File is available in the same current working directory folder

import pandas as pd

df1 = pd.read_csv(
    'hcpuniverse.csv', sep=";"
)  #goback to the previous parent directory files other than present working directory

df1.shape

(3681, 6)

(3681, 6)

If the File is available in the folder curresponding the same working directory location

import pandas as pd

df2 = pd.read_csv(
    '01A-data/hcpuniverse.csv', sep=";"
)  #goback to the previous parent directory files other than present working directory

df2.shape

(3681, 6)

(3681, 6)

OR

import pandas as pd

df3 = pd.read_csv(
    './01A-data/hcpuniverse.csv', sep=";"
)  #goback to the previous parent directory files other than present working directory

df3.shape

(3681, 6)

(3681, 6)

Accessing/loading a file which present in other than current/present working directory exact parent of current

import pandas as pd

df4 = pd.read_csv(
    '../01-data/hcpuniverse.csv', sep=";"
)  #goback to the previous parent directory files other than present working directory

df4.shape

(3681, 6)

(3681, 6)

df4.head()

	c_First_Name	h_First_Name	c_Last_Name	h_Last_Name	c_Title	h_Title
0	Brian	BRIAN	Kelly	KELLY	Medicine	PHYSICAL MEDICINE AND REHABILITATION
1	Rahul	RAHUL	Sharma	SHARMA	Laboratory Director	PHYSICAL MEDICINE AND REHABILITATION
2	Kashif	KASHIF	Khan	KHAN	Anesthesia	GERIATRIC MEDICINE
3	Mark	MARK	Smith	SMITH	Anesthesia	INTERNAL MEDICINE
4	Paul	PAUL	Harris	HARRIS	Apprenticeship in Joinery	FAMILY PRACTICE

	c_First_Name	h_First_Name	c_Last_Name	h_Last_Name	c_Title	h_Title
0	Brian	BRIAN	Kelly	KELLY	Medicine	PHYSICAL MEDICINE AND REHABILITATION
1	Rahul	RAHUL	Sharma	SHARMA	Laboratory Director	PHYSICAL MEDICINE AND REHABILITATION
2	Kashif	KASHIF	Khan	KHAN	Anesthesia	GERIATRIC MEDICINE
3	Mark	MARK	Smith	SMITH	Anesthesia	INTERNAL MEDICINE
4	Paul	PAUL	Harris	HARRIS	Apprenticeship in Joinery	FAMILY PRACTICE

Accessing/loading a file which present in other than current/present working directory grand parent of current path

import pandas as pd
df5 = pd.read_csv('../../00-Python/hcpuniverse.csv',sep=";") #goback to the previous/parent directory files

df5.head()

	c_First_Name	h_First_Name	c_Last_Name	h_Last_Name	c_Title	h_Title
0	Brian	BRIAN	Kelly	KELLY	Medicine	PHYSICAL MEDICINE AND REHABILITATION
1	Rahul	RAHUL	Sharma	SHARMA	Laboratory Director	PHYSICAL MEDICINE AND REHABILITATION
2	Kashif	KASHIF	Khan	KHAN	Anesthesia	GERIATRIC MEDICINE
3	Mark	MARK	Smith	SMITH	Anesthesia	INTERNAL MEDICINE
4	Paul	PAUL	Harris	HARRIS	Apprenticeship in Joinery	FAMILY PRACTICE

	c_First_Name	h_First_Name	c_Last_Name	h_Last_Name	c_Title	h_Title
0	Brian	BRIAN	Kelly	KELLY	Medicine	PHYSICAL MEDICINE AND REHABILITATION
1	Rahul	RAHUL	Sharma	SHARMA	Laboratory Director	PHYSICAL MEDICINE AND REHABILITATION
2	Kashif	KASHIF	Khan	KHAN	Anesthesia	GERIATRIC MEDICINE
3	Mark	MARK	Smith	SMITH	Anesthesia	INTERNAL MEDICINE
4	Paul	PAUL	Harris	HARRIS	Apprenticeship in Joinery	FAMILY PRACTICE

import pandas as pd
df6 = pd.read_csv('../../../DSAIML/hcpuniverse.csv',sep=";") #goback to the previous/parent directory files

df6.head()

	c_First_Name	h_First_Name	c_Last_Name	h_Last_Name	c_Title	h_Title
0	Brian	BRIAN	Kelly	KELLY	Medicine	PHYSICAL MEDICINE AND REHABILITATION
1	Rahul	RAHUL	Sharma	SHARMA	Laboratory Director	PHYSICAL MEDICINE AND REHABILITATION
2	Kashif	KASHIF	Khan	KHAN	Anesthesia	GERIATRIC MEDICINE
3	Mark	MARK	Smith	SMITH	Anesthesia	INTERNAL MEDICINE
4	Paul	PAUL	Harris	HARRIS	Apprenticeship in Joinery	FAMILY PRACTICE

	c_First_Name	h_First_Name	c_Last_Name	h_Last_Name	c_Title	h_Title
0	Brian	BRIAN	Kelly	KELLY	Medicine	PHYSICAL MEDICINE AND REHABILITATION
1	Rahul	RAHUL	Sharma	SHARMA	Laboratory Director	PHYSICAL MEDICINE AND REHABILITATION
2	Kashif	KASHIF	Khan	KHAN	Anesthesia	GERIATRIC MEDICINE
3	Mark	MARK	Smith	SMITH	Anesthesia	INTERNAL MEDICINE
4	Paul	PAUL	Harris	HARRIS	Apprenticeship in Joinery	FAMILY PRACTICE

Accessing/loading a file which present in other than current/present working directory other than grand parent of current path, super path let's say computer account name path

import pandas as pd

df7 = pd.read_csv(
    '~/hcpuniverse.csv', sep=";"
)  #goback to the computer's account named directory(if the file located there) "C:\Users\Administrator" is nothing but ~

df7.head()

	c_First_Name	h_First_Name	c_Last_Name	h_Last_Name	c_Title	h_Title
0	Brian	BRIAN	Kelly	KELLY	Medicine	PHYSICAL MEDICINE AND REHABILITATION
1	Rahul	RAHUL	Sharma	SHARMA	Laboratory Director	PHYSICAL MEDICINE AND REHABILITATION
2	Kashif	KASHIF	Khan	KHAN	Anesthesia	GERIATRIC MEDICINE
3	Mark	MARK	Smith	SMITH	Anesthesia	INTERNAL MEDICINE
4	Paul	PAUL	Harris	HARRIS	Apprenticeship in Joinery	FAMILY PRACTICE

Tables Generation

Tables Generator: https://www.tablesgenerator.com/markdown_tables

IPython.display Module:

How to Display Contents of Different Types in Jupyter Notebook/Lab:
https://coderzcolumn.com/tutorials/python/how-to-display-contents-of-different-types-in-jupyter-notebook-lab

LaTeX/Mathematics codes for statistical symbols

LaTeX/Mathematics:
https://en.wikibooks.org/wiki/LaTeX/Mathematics
https://towardsdatascience.com/write-markdown-latex-in-the-jupyter-notebook-10985edb91fd
https://personal.math.ubc.ca/~pwalls/math-python/jupyter/latex/
https://www.overleaf.com/learn
(\cos (2\theta) = \cos^2 \theta - \sin^2 \theta)

[\cos (2\theta) = \cos^2 \theta - \sin^2 \theta]

$\cos (2\theta) = \cos^2 \theta - \sin^2 \theta$

$$\cos (2\theta) = \cos^2 \theta - \sin^2 \theta$$

$\sum$

$\mathtt{afaf}$

$\mathscr{ABCD}$

$\widehat{AAA}$

$\nsupseteq$

$\Phi, \phi and \varphi$

A matrix in text must be set smaller: $\bigl(\begin{smallmatrix} a&b \\ c&d \end{smallmatrix} \bigr)$ to not increase leading in a portion of text.

[ \begin{matrix} a & b & c \\ d & e & f \\ g & h & i \end{matrix} ]

$\Phi$

$e^{i \pi} + 1 = 0$

$\forall x$

$$ \frac{arg 1}{arg 2} \\ x^2\\ e^{i\pi}\\ A_i\\ B_{ij}\\ \sqrt[n]{arg} $$

Given : $\pi = 3.14$ , $\alpha = \frac{3\pi}{4}\, rad$ $$ \omega = 2\pi f \\ f = \frac{c}{\lambda}\\ \lambda_0=\theta^2+\delta\\ \Delta\lambda = \frac{1}{\lambda^2} $$

$ \delta $ $ \Delta $

$k_{n+1} = n^2 + k_n^2 - k_{n-1}$

$\frac{n!}{k!(n-k)!} = \binom{n}{k}$

$\begin{equation} \frac{ \begin{array}[b]{r} \left( x_1 x_2 \right)\\ \times \left( x'_1 x'_2 \right) \end{array} }{ \left( y_1y_2y_3y_4 \right) } \end{equation}$

$ \int\limits_a^b $

$ 50 \textrm{ apples} \times 100 \textbf{ apples} = \textit{lots of apples}^2 $

$ \begin{equation} x = a_0 + \cfrac{1}{a_1 + \cfrac{1}{a_2 + \cfrac{1}{a_3 + \cfrac{1}{a_4} } } } \end{equation} $

$ \binom{n}{k} = \frac{n!}{k!(n-k)!} $

$$ \mathbf{J} = \frac{d \mathbf{f}}{d \mathbf{x}} = \left[ \frac{\partial \mathbf{f}}{\partial x_1} \cdots \frac{\partial \mathbf{f}}{\partial x_n} \right] = \begin{bmatrix} \frac{\partial f_1}{\partial x_1} & \cdots & \frac{\partial f_1}{\partial x_n} \\ \vdots & \ddots & \vdots \\ \frac{\partial f_m}{\partial x_1} & \cdots & \frac{\partial f_m}{\partial x_n} \end{bmatrix} $$

Embedding Images From Online and Local Folder

Embedding Images From Local Folder:

Above Code we can Use This Below Code as per Obsidian Point of view to display in the Obsidian platform:
![[imgsAudVid/Engelbart.jpg]]

Using Python Code

from IPython.display import Image
i = Image(filename='../03-imgs/1_N1tiuQF9phkp8eYlum9twA.jpeg')
i # list of things you can pass to a dataframe

Download Notebook as PDF, markdown, html and others:

Note: To get above please go through below document to install sufficient software tools:`

nbconvert: Convert Notebooks to other formats:https://nbconvert.readthedocs.io/en/latest/index.html

First Approach

Source: https://mpievolbio-scicomp.pages.gwdg.de/blog/post/2020-12-09_pandoc_vs_nbconvert/
pandoc --to pdf --from ipynb -o Notebook.pdf Notebook.ipynb
OR
Download as PDF via LaTex(.pdf)
OR
Goto Chrome three dots choose print option and Save as PDF

Second Approach

Use Another Approach like below:
https://jupytext.readthedocs.io/en/latest/using-cli.html

Third Approach

Using nbconvert with all necessary installation mentioned below:
https://nbconvert.readthedocs.io/en/latest/install.html

Also check other needed packages to work and export as different file types(md,pdf,html...etc):

Sharing and Publishing Jupyter Notebooks and Exporting the Jupyter Notebook:
https://jupyter-contrib-nbextensions.readthedocs.io/en/latest/install.html https://reproducible-science-curriculum.github.io/publication-RR-Jupyter/02-exporting_the_notebook/index.html https://www.codegrepper.com/code-examples/shell/install+nbextensions
A collection of various notebook extensions for Jupyter:
https://github.com/ipython-contrib/jupyter_contrib_nbextensions

# Sample Code
import matplotlib.pyplot as plt
import numpy as np

xpoints = np.array([1, 8])
ypoints = np.array([3, 10])

plt.plot(xpoints, ypoints)
plt.show()

Embedding Audio Files to the MD File As Per Obsidian Point of View

(Neither displays in ipynb or md file directly from jupyter but it displays in the Obsidian platfrom)
![[imgsAudVid/Excerpt from Mother of All Demos (1968).ogg]]

Embedding Video Files to the MD File As Per Obsidian Point of View

(Neither displays in ipynb or md file directly from jupyter but it displays in the Obsidian platfrom)

import IPython
IPython.display.Audio('imgsAudVid/Excerpt from Mother of All Demos (1968).ogg')

from IPython.display import Video
Video("imgsAudVid/SampleVIDEO1.mp4", embed=True)

from IPython.display import YouTubeVideo
YouTubeVideo('Gx1I_J_Y_Is', width=400, height=200)

Embedding Tweets

(Neither displays in ipynb or md file directly from jupyter but it displays in the Obsidian platfrom)

print("hello world")

hello world

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

new_array = np.logspace(1., 10., num=120, endpoint=True, base=2)

new_array

array([   2.        ,    2.10764261,    2.22107869,    2.34062004,
          2.46659527,    2.59935064,    2.73925109,    2.88668115,
          3.0420461 ,    3.20577299,    3.37831188,    3.56013703,
          3.75174825,    3.95367224,    4.16646404,    4.39070858,
          4.62702224,    4.87605462,    5.13849024,    5.41505049,
          5.70649558,    6.01362662,    6.33728785,    6.67836895,
          7.03780748,    7.41659147,    7.8157621 ,    8.23641662,
          8.67971131,    9.1468647 ,    9.6391609 ,   10.15795312,
         10.70466741,   11.28080658,   11.88795431,   12.52777953,
         13.20204097,   13.91259205,   14.66138591,   15.45048084,
         16.28204588,   17.15836684,   18.08185253,   19.05504144,
         20.08060864,   21.1613732 ,   22.30030592,   23.50053749,
         24.76536709,   26.09827147,   27.5029145 ,   28.98315726,
         30.54306861,   32.18693642,   33.91927935,   35.74485923,
         37.66869421,   39.69607249,   41.83256692,   44.08405027,
         46.45671139,   48.95707223,   51.59200575,   54.36875483,
         57.29495217,   60.37864128,   63.62829855,   67.05285662,
         70.66172888,   74.46483535,   78.47262997,   82.69612933,
         87.14694294,   91.83730514,   96.78010877,  101.98894053,
        107.47811842,  113.26273102,  119.35867902,  125.78271891,
        132.552509  ,  139.68665803,  147.20477627,  155.12752945,
        163.47669555,  172.27522466,  181.54730209,  191.31841483,
        201.61542161,  212.46662673,  223.90185787,  235.95254808,
        248.65182216,  262.03458775,  276.13763125,  290.99971896,
        306.66170362,  323.16663674,  340.5598869 ,  358.88926448,
        378.20515309,  398.56064803,  420.01170228,  442.61728026,
        466.43951995,  491.54390367,  517.99943809,  545.8788439 ,
        575.25875571,  606.21993272,  638.84748069,  673.23108587,
        709.46526156,  747.64960787,  787.88908554,  830.29430441,
        874.9818275 ,  922.07449141,  971.70174396, 1024.        ])

# Silly example data
bp_x = np.linspace(0, 2*np.pi, num=40, endpoint=True)
bp_y = np.sin(bp_x)

# Make the plot
plt.plot(bp_x, bp_y, linewidth=3, linestyle="--",
         color="blue", label=r"Legend label $\sin(x)$")
plt.xlabel(r"Description of $x$ coordinate (units)")
plt.ylabel(r"Description of $y$ coordinate (units)")
plt.title(r"Title here (remove for papers)")
plt.xlim(0, 2*np.pi)
plt.ylim(-1.1, 1.1)
plt.legend(loc="lower left")
plt.show()

import sys; 
print(sys.executable)

C:\Users\Administrator\anaconda3\python.exe

Embedding Slideshare, pinterest, instagram, tumblr, infographics, Handbooks, Mindmaps

Slideshare

Sql Server Basics from rainynovember12

Pinterest

Instagram

View this post on Instagram

A post shared by Data Structure | Algorithm (@python4coding)

tumblr

https://theinsaneapp.tumblr.com/post/687319755653251072/machine-learning-algorithms-read-in-detail

infographics

Handbooks

Mindmaps